LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise

Abstract

Background: Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances.

Glyoxal secondary organic aerosol chemistry: effects of dilute nitrate and ammonium and support for organic radical–radical oligomer formation

“…A syringe pump delivered analyte into the spray chamber at 4 mL min À1 with a capillary temperature of 260 8C and a capillary voltage of À17.50 V. The FT-ICR-MS was calibrated using a Thermo Scientific LTQ-FT external calibration mix. For both positive and negative ion modes, at least 200 scans were collected using the parameters described in Kido Soule et al [47] The transients were processed using SimStich [48] and aligned with MATLAB code provided by Mantini et al [49] as described in Bhatia et al [50] Exact masses were processed with Midas Molecular Formula Calculator (v1.1) as described by Altieri et al [41] to provide elemental formulae of detected species.…”

Advanced Mass Calibration and Visualization for FT-ICR Mass Spectrometry Imaging

“…Here, peak lists from the abundance corrected calibration (apex baseline peak picking 9 S/N 5) are read into Matlab and aligned using the LIMPIC algorithm [39] (3 ppm alignment tolerance) to create a master peak list. Peaks within a window of ±0.001 Da of one of the "master" peaks are considered the same and their intensities are written into an X-Y array used for image generation.…”

Signal Processing Methods for Capillary Electrophoresis

“…The filter has been extended and improved in many different ways (Browne et al 2007). Other digital filters have also been used including the Kaiser filter (Mantini et al 2007) and Gaussian related filters (Leptos et al 2006;Yang et al 2009). In general, digital filters have different time and frequency domain characteristics that are appropriate for different situations and often have parameters that must be optimized so that peak distortion is minimised (Hamming 1983) and the maximal amount of noise removed.…”

“…Thresholding and derivative based peak detection strategies are popular in various areas outside of CE. For example, an auto-threshold based peak detection algorithm for analyzing electrocardiograms was developed (Jacobson 2001); peaks in mass spectrometry data were found by selecting local maximum points with a signal-to-noise ratio above a certain value Mantini et al 2007), and points of local maximum with an intensity sufficiently greater than neighbouring points were classified as peaks (Yasui et al 2003).…”

“…In another published work (Gras et al 1999), selected values from windows were interpolated using cubic splines to estimate the low frequency baseline noise in MALDI-TOF mass spectra. A different strategy was employed with the signal trend being estimated by removing the peaks from a mass spectrum signal (Mantini et al 2007). Other researchers have also applied curve fitting techniques Bernabé-Zafón et al 2005;Gillies et al 2006;Mazet et al 2005).…”